code: def meta_roi_align(
      input,
      rois,
      spatial_scale,
      pooled_height,
      pooled_width,
      sampling_ratio,
      aligned,
  ):
      torch._check(
          rois.size(1) == 5,
          lambda: "rois must have shape as Tensor[K, 5]",
      )
      torch._check(
          input.dtype == rois.dtype,
          lambda: (
              "Expected tensor for input to have the same type as tensor for rois; "
              f"but type {input.dtype} does not equal {rois.dtype}"
          ),
      )
      num_rois = rois.size(0)
      channels = input.size(1)
      return input.new_empty(
          (num_rois, channels, pooled_height, pooled_width)
      )
  
file_path: vision/torchvision/_meta_registrations.py
imports: import functools
  import torch
  import torch._custom_ops
  import torch.library
  import torchvision.extension
label_desc: Use image node_id 3 for calling a global function with example usage: meta_roi_align(input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio, aligned) and returns: input